Different factors limit early‐ and late‐season windows of opportunity for monarch development

Abstract Seasonal windows of opportunity are intervals within a year that provide improved prospects for growth, survival, or reproduction. However, few studies have sufficient temporal resolution to examine how multiple factors combine to constrain the seasonal timing and extent of developmental opportunities. Here, we document seasonal changes in milkweed (Asclepias fascicularis)–monarch (Danaus plexippus) interactions with high resolution throughout the last three breeding seasons prior to a precipitous single‐year decline in the western monarch population. Our results show early‐ and late‐season windows of opportunity for monarch recruitment that were constrained by different combinations of factors. Early‐season windows of opportunity were characterized by high egg densities and low survival on a select subset of host plants, consistent with the hypothesis that early‐spring migrant female monarchs select earlier‐emerging plants to balance a seasonal trade‐off between increasing host plant quantity and decreasing host plant quality. Late‐season windows of opportunity were coincident with the initiation of host plant senescence, and caterpillar success was negatively correlated with heatwave exposure, consistent with the hypothesis that late‐season windows were constrained by plant defense traits and thermal stress. Throughout this study, climatic and microclimatic variations played a foundational role in the timing and success of monarch developmental windows by affecting bottom‐up, top‐down, and abiotic limitations. More exposed microclimates were associated with higher developmental success during cooler conditions, and more shaded microclimates were associated with higher developmental success during warmer conditions, suggesting that habitat heterogeneity could buffer the effects of climatic variation. Together, these findings show an important dimension of seasonal change in milkweed–monarch interactions and illustrate how different biotic and abiotic factors can limit the developmental success of monarchs across the breeding season. These results also suggest the potential for seasonal sequences of favorable or unfavorable conditions across the breeding range to strongly affect monarch population dynamics.


| INTRODUC TI ON
Seasonal windows of opportunity are intervals within a year that provide improved prospects for growth, survival, or reproduction . These seasonal windows reflect a favorable combination of biotic and abiotic factors in space and time, including periods of increased resource availability (e.g., Ogilvie et al., 2017;Visser et al., 2006), reduced predation pressure (e.g., Rasmussen & Rudolf, 2016;Urban, 2007), or more favorable climatic conditions (e.g., Bale et al., 2002;Hunter, 1993). Although the terminology has varied, seasonal windows of opportunity have long been recognized across a wide range of systems (Bale et al., 2002;Elton, 1927;Farzan & Yang, 2018;Hunter, 1993;Ogilvie et al., 2017;Rasmussen & Rudolf, 2016;Urban, 2007;Visser et al., 2006;Yang & Rudolf, 2010). Conceptually, seasonal windows of opportunity represent a qualitative analog of the peaks in a continuous seasonal fitness landscape (Farzan & Yang, 2018;Yang & Rudolf, 2010), and recognize that seasonal periods of increased fitness commonly result from the combined effects of multiple bottom-up, top-down and abiotic factors that change over time (e.g., Farzan & Yang, 2018;. These concepts are also fundamental to the phenological match-mismatch hypothesis (Cushing, 1990): The match-mismatch hypothesis represents a specific case where the window of opportunity for a focal consumer depends on the temporal availability of its resource (Kharouba & Wolkovich, 2020). More broadly, seasonal windows of opportunity represent a temporally explicit extension of the Hutchinsonian niche concept (Hutchinson, 1957;Yang, 2020), analogous to a phenological niche (Post, 2019;Wolkovich & Cleland, 2011, 2014. Seasonal windows of opportunity are constrained by bottom-up, top-down, and abiotic factors. Efforts to quantify the relative contribution of these factors address a fundamental paradigm in ecology and suggest testable predictions about the factors that structure populations and communities. However, in addition to their independent contributions, these factors could also have more complex, interactive, and temporally specific effects on seasonal windows of opportunity. For example, their importance could vary across or within years, or multiple limiting factors could combine sequentially in time. Studying these processes requires a temporally explicit approach: the examination of shorter intervals of time to better understand the dynamics of changing systems (Yang, 2020). Temporally explicit approaches involve a quantitative change in the frequency of observations but have the potential to facilitate qualitative improvements in our understanding of seasonally variable systems.
Here, we present a temporally explicit, high-resolution study of milkweed (Asclepias fascicularis)-monarch (Danaus plexippus) interactions observed across 3 years. The goal of this study is to better understand the factors that limit seasonal windows of opportunity for monarch caterpillars. The population of monarch butterflies in western North America largely overwinters in aggregations along the California coast (Lane, 1993;Leong et al., 2004;Tuskes & Brower, 1978;Yang et al., 2016); in the late winter, these populations become reproductively active and migrate inland from their coastal overwintering sites to find suitable host plants (Dingle et al., 2005;Nagano et al., 1993). This migratory breeding season population expands across their western North American range over multiple generations before largely returning to coastal overwintering populations in the late summer and early fall (Dingle et al., 2005;Yang et al., 2016). Previous experimental studies have suggested that early-season and late-season windows of opportunity on narrowleaved milkweed (Asclepias fascicularis) in the California Central Valley could result from seasonal patterns of growth and defensive trait expression, which affect both the quantity and quality of host plants available to migrating monarchs . These studies suggest that phenological mismatches could create seasonal host plant limitations, especially if periods of high oviposition densities coincide with small host plant sizes . However, previous experimental studies were unable to assess three key factors that could affect monarch developmental success in nature: (1) the effects of inter-and intra-annual climatic variation, (2) the effects of seasonal variation in monarch densities, and (3) the effects of microhabitat heterogeneity. Although it is clear that monarch developmental success can be strongly limited by bottom-up (Flockhart et al., 2015;Nail, Stenoien, et al., 2015;Pleasants & Oberhauser, 2013;Zalucki & Lammers, 2010), topdown (Altizer & Oberhauser, 1999;Hermann et al., 2019;Oberhauser, 2012;Oberhauser et al., 2015;Prysby, 2004) and abiotic (Nail, Batalden, et al., 2015;Stevens & Frey, 2010;York & Oberhauser, 2002;Zalucki, 1982) factors, few studies have examined how multiple factors combine to limit wild milkweed-monarch interactions across the breeding season in a high-resolution, temporally explicit framework.
This study aimed to address three specific questions: (1) How do the developmental prospects of monarchs vary in time, within-and across years? (2) How do the combined effects of bottom-up, topdown, and abiotic factors interact with seasonal variation in monarch density to constrain the timing and extent of seasonal windows of opportunity? and (3) How do climatic variation and microhabitat heterogeneity affect these constraints? 2 | ME THODS

| Field site establishment
In December 2013, we planted a population of 318 individually identified narrow-leaved milkweed (Asclepias fascicularis) at approximately 6.1 m intervals in an approximately 2 km linear transect adjacent to a seasonal irrigation channel (38°34′18.5″N 121°45′29. 6″W) in Davis, CA (Yolo County) USA. These milkweeds were propagated from seedlings using locally collected seeds (Hedgerow Farms, Winters, CA USA). These milkweeds were established as part of a larger effort to create a California riparian plant community including grasses, rushes, sedges (e.g., Bromus carinatus, Carex spp., Distichlis spicata, Elymus spp., Hordeum brachyantherum, Juncus spp., Leymus triticoides, Muhlenbergia rigens, Nassella pulchra, and Poa secunda), shrubs (e.g., Ceanothus cuneatus, Cephalanthus occidentalis, Heteromeles arbutifolia, Rhamnus californica, and Symphoricarpos albus), and trees (e.g., Eucalyptus spp., Fraxinus latifolia, Platanus racemosa, Populus fremontii, Quercus spp., and Salix spp.). This riparian corridor runs adjacent to agricultural fields and a suburban neighborhood, carrying runoff water with a seasonal pattern of generally increased flow during summer irrigation periods and immediately following winter precipitation events ( Figure A1). As a result, this site combines several elements representative of the California Central Valley at a landscape scale.

| Environmental data
We obtained daily temperature maxima, daily temperature minima, and daily precipitation total data for Davis, CA (Global Historical Climatology Network Station USC00042294) over the 20-year period from 1998 to 2018 from the NOAA Climate Data Online Portal (National Centers for Environmental Information, 2018). To create a complete dataset, we imputed missing daily values (1.2% of the available dataset) using a bootstrapping algorithm implemented in the Amelia II package in R (Honaker et al., 2011;R Core Team, 2020) using priors based on daily means and standard deviations.
In addition to this dataset of daily temperature minima and maxima, we also analyzed a second dataset of sub-hourly temperature observations (approximately every 20 min) from the same source to inform a thermal accumulation model of developmental degreesdays and thermal stress exposure for monarchs in the early and late growing season each year. We define the early season as days 90-180 (approximately the end of March to the end of June) and the late season as days 180-270 (approximately the end of June to the end of September) each year. Developmental degree-days for monarchs were calculated using a lower developmental baseline temperature of 11.5°C (Zalucki, 1982) with linear positive thermal accumulation up to 36°C (Masters et al., 1988;York & Oberhauser, 2002). While early studies conducted under constant temperature conditions showed upper developmental thresholds of 28-29°C for monarch development (Barker & Herman, 1976;Zalucki, 1982), subsequent studies have shown that cooler nighttime temperatures allow for continued development under daytime temperatures up to 36°C (York & Oberhauser, 2002), with sublethal thermal stress emerging at temperatures exceeding 38°C (Nail, Batalden, et al., 2015). Thus, we defined developmental degree-days as the product of exposure duration and degrees above 11.5°C up to 36°C, and thermal stress degree-minutes as the product of exposure duration and degrees exceeding the 38°C threshold. Finally, we calculated exposures to temperatures exceeding 42°C, a threshold that has been shown to cause mortality in a very high proportion of monarch caterpillars after a 12 h exposure (Nail, Batalden, et al., 2015). We present both the duration of exposures above this lethal threshold and the accumulation of lethal degree-minutes, defined as the product of exposure duration and degrees greater than 42°C.
We also developed a model of thermal accumulation in narrowleaved milkweed, using a developmental baseline of 11.5°C (based on unpublished data). For the milkweed model, we calculated the accumulation of thermal exposure each year between day 1 and day 163, the day when 75% of milkweed plants exceeded total 50 cm stem length study-wide across all 3 years. We also obtained state-level drought data for the period from 1998 to 2018 from the National Integrated Drought Information System at droug ht.gov (NIDIS, 2019), which classifies the percent of the state under five levels of drought severity over time.
At the site level, we assessed the canopy openness above each milkweed using digital image analysis in ImageJ (Abramoff et al., 2004) of hemispheric photographs taken at approximately 1 m height in July 2016. We also measured representative seasonal changes in the water depth of the irrigation channel at 30-min intervals between April 20, 2017, and July 16, 2018, using a data-logging water depth meter (Onset HOBO U20L). These data were corrected for daily changes in atmospheric pressure using a dataset from the nearest available location (Sacramento Airport, CA, USA) obtained from the NOAA Climate Data Online Portal (National Centers for Environmental Information, 2018).

| Monitoring milkweed-monarch interactions
We collected data at approximately weekly intervals (mean observa- Training sessions represented 4.5 to 6.5 h of in-person training, sometimes spread over 2-3 days or provided during a single daylong workshop event. Training included detailed guidance in identifying and measuring milkweed, identifying and measuring monarch eggs and larvae, and data collection and data entry protocols. Participants were evaluated based on their knowledge of monarch and milkweed biology (e.g., species and stage identification, life history, general ecology) and project-specific skills and protocols (e.g., reading dial calipers, recording data in the field, entering data online, visually estimating percent herbivory). Participants were required to successfully complete an evaluation of knowledge and skills before collecting data for the project. For each week of data collection, available participants were randomly assigned to teams of two to three observers. Each team was randomly assigned to a set of approximately 30-60 consecutively numbered milkweed plants, with sets evenly distributed across the transect. Each team carried a standard field kit including an illustrated milkweed field guide (Rea et al., 2003) and customized, site-specific laminated photo identification guides for narrow-leaved milkweed, monarch instars, and other locally common milkweed-associated arthropods.
Team members alternated between taking measurements and recording data. In teams of three, the third team member documented observations and photographs in a publicly accessible blog. This protocol was designed to facilitate interspersion and minimize the potential for the confounded observer and team effects within and across weeks.
Data were collected with datasheets in the field and entered into shared Google spreadsheets within 24 h of each data collection effort. Undergraduate and graduate student mentors with previous experience in milkweed-monarch research provided guidance in the field during the first weeks of each field season to facilitate data quality and continuity as new participants transitioned into the project. Eric Bastin and Karen Swan provided additional weekly guidance throughout each season, and Louie Yang was available throughout the summer and was present during many weekly data entry sessions to answer additional questions that arose. Participants entered data that they recorded in the field to facilitate handwriting interpretation. We downloaded and analyzed data periodically throughout each field season, using a preliminary R script (R Core Team, 2014) to identify emerging data quality issues and provide rapid data summaries to participants. In 2016 and 2017, we also used the data validation tools in the Google spreadsheet and weekly comparisons of the physical datasheets and the online dataset to prevent data entry errors.
We excluded measurements, which were likely to have resulted from data entry errors from the analysis. These included 0.11% of stem diameter measurements (96 of 86,363) that exceeded 15 mm (Z-score >5.65) and 0.02% of stem length measurements (21 of 86,945) that exceeded 150 cm (Z-score >4.78). In most cases, these data appear to have resulted from missing decimal points. Excluding these data likely had a negligible effect on the overall analysis because they represent a very small proportion of the overall dataset and because our analysis used multiple measurements per plant as subsamples to calculate an observation-level mean for each milkweed at each visit. We did not detect data entry errors in other metrics of plant or monarch development.

| Analysis of milkweed growth and phenology
Because narrow-leaved milkweed growth with multiple lateral stems, we used two metrics to estimate plant size. Total stem length estimated the cumulative length of stems on the branching growth form of narrow-leaved milkweed, while total cross-sectional stem area provides a cumulative metric of stem thickness. The total stem length of each plant at each observation was estimated as the product of the mean observed individual stem length and the total stem count. The total cross-sectional stem area of each plant at each observation was similarly estimated as the product of the mean observed cross-sectional stem area and the total stem count. The total stem count included all shoots (main stems and lateral stems) with a nonsenescent length greater than 5 cm. We calculated the mean stem length and mean stem diameter from measurements of 10 haphazardly selected stems per plant unless fewer stems than 10 stems were available. These stems were chosen to provide a representative subsample of the stem length distribution on each plant.
We aggregated the resulting dataset on annual and weekly scales to summarize all available milkweed and monarch metrics each week and for each of the 3 years in the study. All analyses were conducted in R (R Core Team, 2020) using the tidyverse package (Wickham et al., 2019).
The emergence phenology of milkweed was quantified as the mean date when plants exceeded a total stem length of 5 cm. To identify the period of increased host plant biomass (i.e., the viable season length) each year, we defined an interval bounded by a threshold of plant size (the date when the population mean exceeded a threshold total stem length of 50 cm) and a threshold of plant senescence (the date when the population mean fell below 80 percent greenness). These boundary conditions place approximate and qualitative milestones informed by previous studies in this system  to quantify a period of increased host plant viability for monarch development.
We analyzed the role of canopy openness as a microhabitat variable affecting milkweed emergence, growth, and phenology using linear and generalized linear models (GLMs) with canopy openness, year, and their interaction as predictors. Our model of milkweed emergence phenology used the day of year when each plant exceeded a total stem length of 5 cm as the response variable.
In this and all subsequent linear models, assumptions of residual normality and homoscedasticity were assessed using Q-Q plots and residual plots. A second linear model examined the day of year when milkweeds exceed a total stem length greater than 50 cm. A third growth model used the maximum total stem length attained by each plant as a response variable. This model used a gamma conditional distribution with a log-link function. The gamma distribution is flexibly and appropriately applied to positive, continuous data with an approximately log-normal distribution. A fourth, final linear model examined the day of year when plants first showed greenness values less than 80%. In all models, nonsignificant (α = 0.05) interaction terms were removed before examining the main effects. When significant interaction effects were present, we examined simple effects separately.

| Analysis of monarch growth and phenology
Weekly monarch observation counts provide information about the relative abundance of monarchs across each year and allow comparisons between years. We examined annual and seasonal differences in egg and caterpillar observation counts considering the effects of year, season (early vs. late), and their interaction using separate GLMs with Poisson conditional distributions and log-link functions.
We chose the Poisson distribution a priori due to the count-based (positive integer) response variables.
In order to visualize seasonal patterns in the survivorship of monarchs, we examined the ratio between the maximum number of fifth instar caterpillars observed per week divided by the maximum number of eggs observed per week (fifth instar: egg) for each season × year combination. This ratio provides a relative metric indicative of the proportion of observed eggs that are later observed as fifth instar larvae. We found qualitatively similar patterns when considering ratios of other stages (fifth instar: first instar and fifth instar: second instar).
To assess the potential for seasonal variation in oviposition site selection based on milkweed size, we compared the mean size of milkweeds with and without monarch eggs present each week. We quantified this comparison using a log ratio (log s e ∕ s 0 ), where s e represents the mean size of milkweeds with eggs present and s 0 represents the mean size of milkweeds without eggs in a given week.
This ratio provides a metric of apparent host plant size selectivity where positive values reflect a preference for comparatively larger host plants, while negative values reflect a preference for comparatively smaller host plants. We tested for significant deviations of this ratio from zero in each week using a Fisher-Pitman permutation test, implemented in the R package coin (Hothorn et al., 2008).
In addition, we evaluated if the observed distributions of monarch egg and caterpillars counts per plant deviated from the random null assumption of a Poisson distribution; this test assesses the degree to which monarch observations were clumped, random or overdispersed among host plants.
We further examined the effect of canopy openness on the total annual count of monarch egg and larval observations per plant using separate Poisson GLMs with log-link functions. These models considered canopy openness, year, and their interaction as predictors. A subsequent Poisson GLM considered canopy openness, year, season (early vs. late), and their second-order interactions as predictors. We also examined the effects of milkweed size (maximum total stem length) and milkweed phenology (first day of each year with a total stem length greater than 50 cm) on the total annual count of monarch larval observations per plant using a Poisson GLM; both models also assessed year effects and their interactions using Type II sums of squares. We also developed a generalized additive model (GAM) that included milkweed phenology (the timing of the median size threshold) as a predictor variable for the total annual count of monarch caterpillars to assess the potential for nonlinear effects on total larval observation counts across each year. We compared this GAM model with its GLM counterpart using AIC. The AIC favored the GLM, and we report only those results.
We analyzed the notes field of our dataset to quantify the proportion of notes each week that included observations of taxa that were potential predators or competitors of monarch eggs or caterpillars during our study. We used the same approach to quantify the proportion of notes that included observations of adult monarchs each week. Observed predatory taxa were small milkweed bugs (Lygaeus kalmii), ladybird beetles (Coccinellidae), wasps (Vespidae), jumping spiders (Salticidae), crab spiders (Thomisidae), ants (Formicidae), hoverfly larvae (Syrphidae), lacewings (Chrysopidae), mantids (Mantodea), and earwigs (Dermaptera). Observed herbivorous taxa were oleander aphids (Aphis nerii), small milkweed bugs (Lygaeus kalmii), large milkweed bugs (Oncopeltus fasciatus), blue milkweed beetles (Chrysochus cobaltinus), milkweed longhorn beetles (Tetraopes basalis), planthoppers (Fulgoromorpha) and leafhoppers (Cicadellidae). Small milkweed bugs (Lygaeus kalmii) were counted as both predatory and herbivorous taxa due to their strongly omnivorous habits (Root, 1986). We examined two binomial GLM models to examine the effects of canopy openness, year, and their interaction on the proportion of notes that included predator or competitor observations, respectively.
In a final set of models, we evaluated the relative and combined effects of key factors hypothesized to affect egg and caterpillar observation counts. First, we evaluated a GLM considering milkweed size (maximum total stem length), thermal stress exposure (degreeminutes ≥38°C), exposure to predators (proportion of notes with predators observed), season (early vs. late), and all pairwise interaction effects with the season. This model used a Poisson conditional distribution with a log-link function to account for the count-based response variable. If this analysis suggested a significant seasonal interaction effect, we subsequently compared separate models focused on the early and late seasons.

| Environmental data
Climatic observations show a Mediterranean pattern of cool, wet winters and hot, dry summers during the study period ( Figure A2).
Water levels in the lower channel were consistent in the summer and intermittent in the winter ( Figure A1 F I G U R E 2 Mean milkweed total stem length and percent green at weekly intervals across three growing seasons. Point size and the vertical axis indicate the weekly mean plant size, and point color indicates the weekly mean percent green. The blue region represents a period of increased host plant availability for monarch development bounded by the mean date when plants exceeded 75 cm total stem length on the left and the mean date when percent green was declined below 80% on the right. Solid vertical lines indicate the start and end of observations at each season. The dotted vertical line represents day 180, which is used to separate the early and late season in these analyses. F I G U R E 3 Effects of canopy openness on (a) the phenology of milkweed emergence, (b) the timing of milkweed growth, (c) maximum total stem length and (d) timing of senescence. Canopy openness was generally associated with earlier milkweed emergence, earlier growth to a viable host plant size, and larger maximum milkweed sizes across the season population showed reduced total stem lengths immediately after the mowing event, and we did not observe a substantial decline in population mean total stem length immediately afterwards ( Figure 3).
Thus, this disturbance probably had a relatively small effect on the phenology of the overall milkweed population in our study, though it may have delayed the assembly of the predator community .
The timing of milkweed emergence varied strongly among years openness × year, p = .23) and the ranked phenology of milkweed plants was highly correlated between years (r = .59, p < .0001, Figure A7).
We observed similar patterns of milkweed phenology with measures of total stem cross-sectional area and reproduction (flowering and seed pod production). Stem cross-sectional area was dynamic across each season ( Figure A8), but the annual mean was markedly  had two eggs, and the remaining 19% had three to 12 eggs present ( Figure A13a). Looking at these same data from an egg perspective, 34% of eggs were observed singly, 23% were observed in pairs, and 43% were observed in densities greater than two eggs per plant.
Similar patterns were observed with caterpillars ( Figure A13b)

| Spatiotemporal patterns and canopy openness
The

| Leaf damage
The mean percent leaf area removed across the population was generally low (Figure 8)  6.1% (2015: 6.8%, 2016: 6.2%, 2017: 5.0%). Across all plants, the weekly mean percent leaf area removed ranged from 0.1% to 13.8%; for plants with monarch caterpillars present, this metric ranged from 0% to 34.8% (Figure 8). However, the distribution of damage estimates was strongly skewed with high variance, with some plants experiencing much higher rates of herbivory throughout each year ( Figure 8). Across the study, the annual maximum leaf damage was positively correlated with the count of caterpillar observations on that plant (r = .34, p < .0001, Figure A18).

| Predatory taxa
Observations of predatory taxa varied across the years of this study ( Figure A19)

| Herbivorous taxa
By comparison, the community of herbivorous taxa was largely Milkweed plants with greater canopy openness generally had more observations of herbivorous taxa (p < .0001, Figure 9b).
Observations of herbivorous taxa differed by year (p < .0001) and were generally more common in 2017 than in 2015 or 2016. The effect of canopy openness did not differ significantly among years (p = .56), showing a predicted 59% increase across the observed range of canopy openness values (30-100%).

| Combined analysis
A model of egg observation counts using milkweed size (maximum total stem length), thermal stress exposure (degree-minutes ≥38°C), exposure to predators (proportion of notes with predators observed),

season (early vs. late), and all pairwise interaction effects with season
showed that the effect of milkweed availability differed significantly in the early and late season (p = .001). As a result, we analyzed earlyand late-season data in separate models. These models showed that milkweed availability was associated with egg counts positively in the early season and negatively in the late season (early: p < .0001; late: p = .035, Figure 10a). Thermal stress did not have a significant effect on egg counts in the early season but showed a strong negative effect in the late season (early: p = .25; late: p < .0001, Figure 10a). Additionally, exposure to predators had a negative effect on egg counts in the early season (p = .029, Figure 10a); this effect remained negative but not significant in the late season (p = .18, Figure 10a).

A parallel model of caterpillar observation counts indicated
that the effects of thermal stress (p = .0007) varied by season. The effects of predator exposure also showed a marginally significant interaction with the season (p = .07). We subsequently analyzed models that considered the early and late seasons separately; these models showed significant positive effects on milkweed availability in both seasons (early: p < .0001; late: p < .0001, Figure 10b). The effects of thermal stress exposure were widely divergent in the early and late season, showing marginal positive effects in the early season (p < .09), followed by strong negative effects in the late season (p < .0001, Figure 10b). Exposure to predators had a nonsignificant negative effect in the early (p = .81) and a significant positive effect in the late season (p = .005, Figure 10b).

| DISCUSS ION
Our study has three key findings. First, this study documents seasonal windows of opportunity in the wild, migratory western monarch population. Second, these seasonal windows appear to be constrained by different factors in the early and late part of each breeding season. Third, climatic and microclimatic variation strongly shaped the timing and relative importance of different limiting factors in this study. Here, we examine each of these findings and consider their implications in the context of a declining western monarch population.

| Seasonal windows of opportunity
Our results show early-and late-season windows of opportunity for monarch development on narrow-leaved milkweed ( Figure 4). Although the specific timing of these windows var- ied from year to year, all 3 years showed 2-to 4-week windows of higher recruitment in the early and late season, separated by a mid-summer period with substantially lower developmental prospects (Figure 4). These windows do not represent the direct offspring of two successive generations, as they were separated by more than 12 weeks (Figure 4), while the total (egg to adult) development time of monarchs is generally less than 22 days (York & Oberhauser, 2002;Zalucki, 1982). Adult monarchs were present at our site throughout the breeding season and were actually commonly observed during a period of low egg and larval densities in the mid-summer ( Figure A11).
Thus, the observation of seasonal windows in this study seems to suggest periods with increased recruitment potential, rather than simply reflecting a seasonal pattern of adult monarch density at our site.
However, in contrast to previous experimental studies in this system , the early-season windows of opportunity in this study were largely unrealized; only a small proportion of these monarch eggs survived to be observed as later larval instars (Figures 4 and 5). Thus, our current study suggests an early-season window characterized by high recruitment potential (i.e., oviposition) but ultimately low survivorship (low caterpillar observations). This difference suggests the possibility of a densitydependent constraint on monarch success resulting from high oviposition densities in the early season. More broadly, the observed F I G U R E 1 0 Comparison of standardized effect sizes for early-and late-season GLMs of (a) egg and (b) caterpillar observation counts. Effect sizes are standardized by 1 SD, and lines represent the 95% confidence interval

| Early-season constraints
In the early season of each year, we observed a period of high oviposition density on a subset of host plants (Figures 4 and A10), with relatively low survivorship to later larval stages ( Figure 5). to use environmental cues that maintained phenological correspondence between their spring migration and the emergence of early-season milkweed shoots (Guerra & Reppert, 2015;Reppert & de Roode, 2018). Cueing mechanisms to maintain this correspondence could be adaptive if optimal oviposition timing reflects a balance between the dynamic constraints of resource quality and quantity. Although these ontogenetic patterns vary by milkweed species, defensive traits such as latex exudation, trichome density, and leaf toughness generally seem to increase through the early season (Pearse et al., 2019;. Caterpillars feeding on young plants with relatively weak defensive traits show initially higher survivorship and significantly faster growth . In contrast, monarch neonates feeding on mature intact plants experience high rates of mortality during initial feeding, while neonates feeding on leaves with experimentally reduced latex exudation experienced significantly higher survival and growth Zalucki, Malcolm, et al., 2001). These studies suggest a pattern of declining resource quality over time, consistent with ontogenetic patterns that have been observed in other herbaceous plants (Barton & Koricheva, 2010;Boege & Marquis, 2005). Thus, early-season milkweeds likely provide relatively high-quality resources with relatively weak defensive traits , potentially favoring earlier oviposition. However, resource quantity constraints create a simultaneous selection pressure in the opposite direction.
Previous studies on narrow-leaved milkweed indicate that, despite their relatively high initial survivorship, caterpillars on young host plants eventually experience reduced survivorship due to the small size of individual plants . Thus, milkweeds may present a phenological challenge of simultaneously declining host plant quality and increasing quantity each season; oviposition too early increases the probability of starvation, while oviposition too late incurs the developmental costs of increasing plant defensive traits .
We hypothesize that these rapidly changing milkweed traits create a dynamic landscape where ovipositing females are selecting for trait combinations that balance resource quality and quantity. We observed a patchy distribution of egg and caterpillar observations ( Figure A14, Movies S1 and S2) where most monarch eggs (66%) were observed in densities of two or greater ( Figure A13). Although recently emerged host plants are relatively small in the early season, both egg and caterpillar counts were highest on the largest available milkweeds in the early season (Figures 6 and 10), which tended to be associated with more open canopy environments (Figures 3 and 7, A16 and A17). This spatial patchiness was unexpected given previ-  (Doak et al., 2006); in both of these cases, the vast majority of host plants were not selected for oviposition. These observations of oviposition site selectivity are also consistent with previous studies indicating that monarchs favor younger but also taller and rapidly growing host plants for oviposition (Zalucki & Kitching, 1982), and studies showing the preferential oviposition and increased developmental success of monarchs on the rapid regrowth of milkweeds following physical disturbance (Fischer, 2015;.  (Flockhart et al., 2012;Nail, Stenoien, et al., 2015). Thus, while the preference for larger early-season host plants observed in our study might reflect past selection pressures to reduce the risk of starvation, the resulting patchiness of monarch oviposition ( Figures 6, A13 and A14, Movie S1) could also potentially exacerbate seasonal host plant limitation by concentrating monarch herbivory in space, contributing to a pattern of "limitation by selectivity." In addition to this concentration of herbivore demand in space, our data also suggest that monarch oviposition was concentrated in time ( Figure 4), with especially high rates of oviposition during a short period in the early growing season when even the largest plants in the population were relatively small ( Figure A10). A similar pattern of seasonally compressed oviposition activity has been observed in the eastern migratory range , but the proximity of overwintering sites in the western range could increase the potential for relatively synchronized migratory arrivals and high egg densities. A pattern of more temporally compressed oviposition could also result if coastal overwintering populations are disaggregating before most inland milkweed host plants are available. While the departure timing of eastern monarchs from Mexican overwintering sites does not appear to have shifted in recent years , western monarchs have shown earlier first flight observations in association with warmer, wetter winter temperatures (Forister & Shapiro, 2003). While it seems plausible that the spring migration of western monarchs has advanced under ongoing climate change, it is unclear whether the growth phenologies of western milkweeds have kept pace. Their growth phenology has not advanced significantly in the east (Howard, 2018), and considerably less is known about the emergence phenology of the many milkweed species in the West.
If the phenological advances of migrating western monarchs are increasingly mismatched with the growth phenology of their milkweed host plants, this could create the potential for an "ecological crunch" period with transiently increased resource competition (Wiens, 1977).

| Late-season constraints
We observed significantly lower densities of eggs and significantly higher densities of caterpillars in the late season (Figures 4 and 5), with substantial inter-and intra-annual variation in the timing and magnitude of the late-season windows. We hypothesize that these patterns may have been associated with the combined effects of direct thermal stress (Figures 1 and A4) and changing host plant defensive traits  but were unlikely to be constrained by the total availability of milkweed biomass (Figures 2 and 8).
Exposures to stressful temperatures were much higher in the late season than in the early season (Figures 1 and A4), and our model in detectability Oberhauser et al., 2001).
While natural enemies (predators, parasitoids, parasites, pathogens) are known to strongly limit the survivorship of monarch eggs and caterpillars generally (Altizer & Oberhauser, 1999;Hermann et al., 2019;Oberhauser, 2012;Oberhauser et al., 2015;Prysby, 2004), their specific role in constraining late-season windows of opportunity is less clear. Observations of predaceous taxa were highly variable between and within years, both in terms of their proportion of noted observations and their taxonomic composition ( Figure A19). The relative importance of seasonal variation in predation pressure relative to other constraints on caterpillar recruitment (e.g., initial oviposition density and resource limitations) remains uncertain, and future experimental studies will be necessary to examine the relative contribution of abiotic (climatic), bottom-up (host plant-mediated) and top-down (natural enemy) constraints.
Interactions with other herbivores could also have affected lateseason windows of opportunity in our study. In all 3 years of this study, observations of other herbivore taxa increased from relatively low densities (<0.3) in the early season to relatively high densities (>0.9) by the end of the late season ( Figure A20). This seasonal pat-

| The fundamental effects of climate
The climatic effects observed in our study were both complex and fundamental, suggesting specific effect pathways that varied across the early and late seasons of each year. In addition to their direct abiotic effects, this climatic (and microclimatic) variation likely played a fundamental role in setting the stage for subsequent biotic interactions. For example, while drought conditions limited milkweed growth they also advanced milkweed phenology (Figures 2 and A9), and may have increased foliar nitrogen and reduced key defensive traits (Couture et al., 2015). In our study, drought conditions did not seem to limit monarch success in any simple sense and may have had their strongest effects via changes in host plant phenology and quality, rather than productivity. Similarly, warmer later winter temperatures were associated with advanced milkweed phenology in the early season (Figures 2 and A3) but may have also increased latex exudation (Couture et al., 2015) and exposure to stressful temperatures in the late season (Figures 1 and 10, and A4). While experiments will be necessary to assess causation, our study suggests the value of a high-resolution seasonal perspective to understand changing climatic effects between and within years.
Our analysis of canopy openness further illustrates the funda- Conversely, monarchs in more exposed locations may also have experienced greater direct thermal stress during years with more intense heatwaves (Figure 7b), and higher predator and herbivore densities ( Figure 9). These patterns illustrate the complex pathways by which microclimatic variation can affect monarch development but also suggest that microhabitat variability in heterogeneous habitats could buffer species interactions under changing climatic conditions (e.g., Rytteri et al., 2021).
Disentangling the direct and indirect (i.e., mediated via the host plant or the surrounding community) effects of climatic variation on monarch development is likely to be difficult (Boege & Marquis, 2006;Despland, 2018;Kharouba & Yang, 2021) (Yang, 2020;Yang et al., 2021) to examine how climatic variation shapes the seasonal timing and magnitude of abiotic, bottom-up, and top-down constraints on species interactions.

| Context and conclusions
The 3 years of this study (2015)(2016)(2017)  The results of this study suggest that climatic variation among years and across seasons plays a foundational role in the timing and success of monarch developmental windows. These results seem to be in contrast to previous continental-scale modeling efforts that did not detect a strong signal of climatic factors in historic monarch population declines in the east (Flockhart et al., 2015;Stenoien et al., 2018;Zalucki et al., 2015), though climate factors have been associated with phenology and growth of the monarch population in specific parts of the eastern range (Zipkin et al., 2012;Zylstra et al., 2021). In comparison, studies in the western range generally suggest a stronger role for climatic factors, though the relative contributions of climatic and nonclimatic factors have been difficult to separate Espeset et al., 2016;Stevens & Frey, 2010 (Nail, Batalden, et al., 2015;York & Oberhauser, 2002;Zalucki, 1982), which allowed us to infer lethal and sublethal thermal constraints. In addition, it is also possible that the timing of our study allowed us to observe the effects of direct thermal stress that have become more apparent in recent years. Nine of the ten warmest years in the global record occurred in the past decade (National Centers for Environmental Information, 2021), and the frequency and intensity of heatwave events have continued to increase globally (IPCC, 2021) and in California (Gershunov & Guirguis, 2012).
Our findings are consistent with previous studies suggesting seasonally specific limits on monarch recruitment (Espeset et al., 2016;Zipkin et al., 2012), with particular emphasis on the early season Espeset et al., 2016;Zylstra et al., 2021). Previous studies have suggested that warmer winter and spring conditions generally favor monarchs (Espeset et al., 2016, Zipkin et al., 2012. Our analysis has a limited ability to assess this pattern, but our observations are at least partly consistent; substantially higher monarch observations occurred during a year with marginally warmer early-spring conditions (Figures 5 and A3), and individual plants with warmer, more open canopies were generally associated with more monarch observations (Figure 7). We suggest that these patterns may be due to climate-driven advances in the growth phenology of host plants; climatic conditions that allow for earlier host plant growth were associated with improved monarch success, potentially by increasing the temporal overlap between consumer demand and resource availability. Future experimental studies will be necessary to evaluate this hypothesis. However, our study also suggests an important caveat about the emergence of potentially  A2). However, our findings are consistent with the observation of generally advancing phenologies and increasing abundances of diverse butterfly communities in this region in response to previous drought conditions (Forister et al., 2018). Our study could help to resolve these apparently conflicting findings, as these differences are consistent with the complex, combined effects of temperature and precipitation on milkweeds and monarchs, and spatiotemporal differences in their effects. In our study, cooler and wetter earlyspring conditions (as in 2017) were associated with delayed milkweed growth but ultimately larger plants and delayed senescence The degree to which monarchs experience seasonal host plant limitation more broadly remains unclear. In nature, the possibility of seasonal host plant limitation depends on the phenology of monarch migration (Dingle et al., 2005;Forister & Shapiro, 2003) relative to the phenology of milkweed emergence (Howard, 2018;Pearse et al., 2019;, and the interacting effects of milkweed densities (Flockhart et al., 2015;Stenoien et al., 2015;Zalucki & Lammers, 2010), monarch densities (Flockhart et al., 2012;Nail, Stenoien, et al., 2015;Stenoien et al., 2015), and host plant selection behavior (Jones & Agrawal, 2019;Zalucki & Kitching, 1982). However, selective monarch oviposition ( Figure 6) could suggest a mechanism for host limitation consistent with the appearance of generally high milkweed availability and low herbivory (Figure 8).
Future studies will be necessary to evaluate the degree to which seasonal host plant limitation is occurring in the western range and the specific mechanisms that might contribute to this limitation.
The unique value of this current study emerges from the explicit examination of seasonality, which required repeated observations with high temporal resolution. This high-resolution observational approach provided a way to examine seasonal and density-dependent dynamics while also developing temporally explicit, sequential hypotheses to guide future studies. We hope that these efforts improve our understanding of the factors that constrain monarch development across the season, and the potential for future population resilience.

F I G U R E A 7
Correlation in 2016 and 2017 ranked milkweed phenologies measured as the first day each year when a plant's total stem length exceeded 50 cm. The significant correlation (r = .59, p < .0001) suggests that the relative phenology of milkweed plants was consistent between these two years; early plants tended to be early in both years. Early plants also tended to be larger (indicated with point size) and generally supported more larval monarch observations (indicated with point color) than later growing, smaller plants F I G U R E A 1 2 Early versus late season differences in the observation ratios of fifth instar caterpillars relative to first and second instar caterpillars are consistent with those seen with eggs ( Figure 5)